A bidder participating in an auction has to make a number of decisions. Primarily, he/she must evaluate the value of the item(s) being sold (or bought in the instance of a procurement auction) based on the information he/she possesses. Some of this information may be bids of rivals that are revealed during the course of the auction. Second, he/she needs a procedure to assess the probability of winning as a function of alternative bids he may submit in the auction. The best actions typically depend on the rules of the auction (e.g., auction format and reserve price) and on the specifics of the competitive situation characterized typically by the rival bidders' attitudes towards risk, the distribution of bidders' private information and other relevant random elements.
A bidder at an auction, whether buying or selling, can improve his expected payoff by submitting a bid that takes into consideration various factors including the auction format, the reserve price, the number of rival bidders and their value distributions and risk attitudes.
As is known, the outcome of an auction (e.g., who gets what, who pays how much) is determined by bidding behavior of bidders. Bidding behavior depends on a number of factors including the auction rules. Different auction rules induce different behavior on the part of the bidders. A bidder's behavior under a given collection of auction rules in turn is determined by the bidder's private information. The structure of the private information held by the bidders is thus a key factor in evaluating alternative auction rules. This fundamental element of the auction environment is not directly observable and has to be estimated from available data.
Currently, the decisions on bidding are left entirely to the person bidding on the auctioned item(s). There is little systematic data analysis to guide these decisions. Given the multiplicity of items bought/sold through auctions, it is typically too costly to hire expert analysts to determine bids for each case. Furthermore, a fixed method for determining a bid is rarely optimal for every case to which it is applied. Bidders typically must resort to decisions based on personal feelings and instinct, and even when the bidder has reliable information about various aspects of the auction and his rivals, he usually does not know how to use that information to arrive at a bid amount.
Currently, there is not an integrated data collection, modeling, estimation and optimization solution for selecting the bid optimally based on structural econometric analysis of available data. All decisions must be based on personal knowledge rather than a systematic analysis. As a result, a determination of an optimal bid is often guesswork and may not provide optimal results.
Accordingly, there exists a need for an automated estimation and optimization solution for selecting the best bid in an auction. A need exists for a method and/or system that provides automated decision support for selecting the best bid based on structural analysis of data from related auctions. A need also exists for a method and/or system that accomplishes the above needs and provides a method and/or a system for estimating the likely outcomes under alternative bidding strategies and to identify the best bidding strategy for a multiplicity of auction rules and competitive environments.